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PyTorch Documentation Search Tool

by seanmichaelmcgee
GitHub
Python
1
OverviewInspectNewSchemaRelated ServersReviewsScore
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Integrations

  • Uses OpenAI's API for generating embeddings to power the semantic search functionality over PyTorch documentation

  • Provides semantic search capabilities over PyTorch documentation, allowing users to find relevant documentation, APIs, code examples, and error messages using vector embeddings and semantic similarity

PyTorch Documentation Search Tool (Project Paused)

A semantic search prototype for PyTorch documentation with command-line capabilities.

Current Status (April 19, 2025)

⚠️ This project is currently paused for significant redesign.

The tool provides a basic command-line search interface for PyTorch documentation but requires substantial improvements in several areas. While the core embedding and search functionality works at a basic level, both relevance quality and MCP integration require additional development.

Example Output

$ python scripts/search.py "How are multi-attention heads plotted out in PyTorch?" Found 5 results for 'How are multi-attention heads plotted out in PyTorch?': --- Result 1 (code) --- Title: plot_visualization_utils.py Source: plot_visualization_utils.py Score: 0.3714 Snippet: # models. Let's start by analyzing the output of a Mask-RCNN model. Note that... --- Result 2 (code) --- Title: plot_transforms_getting_started.py Source: plot_transforms_getting_started.py Score: 0.3571 Snippet: https://github.com/pytorch/vision/tree/main/gallery/...

What Works

✅ Basic Semantic Search: Command-line interface for querying PyTorch documentation
✅ Vector Database: Functional ChromaDB integration for storing and querying embeddings
✅ Content Differentiation: Distinguishes between code and text content
✅ Interactive Mode: Option to run continuous interactive queries in a session

What Needs Improvement

❌ Relevance Quality: Moderate similarity scores (0.35-0.37) indicate suboptimal results
❌ Content Coverage: Specialized topics may have insufficient representation in the database
❌ Chunking Strategy: Current approach breaks documentation at arbitrary points
❌ Result Presentation: Snippets are too short and lack sufficient context
❌ MCP Integration: Connection timeout issues prevent Claude Code integration

Getting Started

Environment Setup

Create a conda environment with all dependencies:

conda env create -f environment.yml conda activate pytorch_docs_search

API Key Setup

The tool requires an OpenAI API key for embedding generation:

export OPENAI_API_KEY=your_key_here

Command-line Usage

# Search with a direct query python scripts/search.py "your search query here" # Run in interactive mode python scripts/search.py --interactive # Additional options python scripts/search.py "query" --results 5 # Limit to 5 results python scripts/search.py "query" --filter code # Only code results python scripts/search.py "query" --json # Output in JSON format

Project Architecture

  • ptsearch/core/: Core search functionality (database, embedding, search)
  • ptsearch/config/: Configuration management
  • ptsearch/utils/: Utility functions and logging
  • scripts/: Command-line tools
  • data/: Embedded documentation and database
  • ptsearch/protocol/: MCP protocol handling (currently unused)
  • ptsearch/transport/: Transport implementations (STDIO, SSE) (currently unused)

Why This Project Is Paused

After evaluating the current implementation, we've identified several challenges that require significant redesign:

  1. Data Quality Issues: The current embedding approach doesn't capture semantic relationships between PyTorch concepts effectively enough. Relevance scores around 0.35-0.37 are too low for a quality user experience.
  2. Chunking Limitations: Our current method divides documentation into chunks based on character count rather than conceptual boundaries, leading to fragmented results.
  3. MCP Integration Problems: Despite multiple implementation approaches, we encountered persistent timeout issues when attempting to integrate with Claude Code:
    • STDIO integration failed at connection establishment
    • Flask server with SSE transport couldn't maintain stable connections
    • UVX deployment experienced similar timeout issues

Future Roadmap

When development resumes, we plan to focus on:

  1. Improved Chunking Strategy: Implement semantic chunking that preserves conceptual boundaries
  2. Enhanced Result Formatting: Provide more context and better snippet selection
  3. Expanded Documentation Coverage: Ensure comprehensive representation of all PyTorch topics
  4. MCP Integration Redesign: Work with the Claude team to resolve timeout issues

Development

Running Tests

pytest -v tests/

Format Code

black .

License

MIT License

This server cannot be installed

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security - not tested
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hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Provides semantic search capabilities over PyTorch documentation, enabling users to find relevant documentation, APIs, code examples, and error messages through Claude Code integration.

  1. Current Status (April 19, 2025)
    1. Example Output
  2. What Works
    1. What Needs Improvement
      1. Getting Started
        1. Environment Setup
        2. API Key Setup
      2. Command-line Usage
        1. Project Architecture
          1. Why This Project Is Paused
            1. Future Roadmap
              1. Development
                1. Running Tests
                2. Format Code
              2. License

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